{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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L+52OQ3VaJPKsZ3hu8VkAIqfBldZFAELXhT0NBtlLmFsQbUTTWLpdkZ6LgUdb\ntX69/R7Xr4oD9aMffJzSGdEQdLt1/EADQ6KY1Ih2wQsKkI2+F4fNfHBz7e7g7wEGc1Gr1Th9+jQA\n7373u6lWReMWx3H+nk6nQ6MhWpbNzU3W12X/pWmaa6nSNM21VMaYXDOVJElO57Isy/MYRVE0svPy\n4tkKTiy0YCZdIA7EnFEN1uCE7PnL9SvsNIVGeBZsSfrnFQxuJm0/8Y4YP9R8TP2UnVTeGRYdMiPz\ntt6CTiz01+skzGtOq+52C7pqgk48Hlt8AoDtfoU9I3PrFmOOHxFNZsm1vKZO86PAMQWygb0QQ6Z0\n/zc/8W/4V//+XwHQjtu5Zmpvcy+P4r4U+DRbbe17QL8nY9yPolzP49qEuCHaxSuzk5z76gkAyqUJ\nEtUMJ3FMR2notaUrZKrhKoUFTi6KeTzp9ZnRKL8Pfcu30muPrtGAPgeaqYOoHmdIpW6MwQwiaLM2\ns67OBRH7HZkvr1zFR7SE80EDm8n+a3er+N6AdkZot0ixZOqYHrhZbvmJbuGCbknADN0byVIgeHW7\nRoCcu72wy+KcrJ8F32dtSealFXWxqbjjLNR8Fo7IeEbdi4Su9CtO9qgVZQ+dKhYJ1qX9r9Z9GpkG\nxVROUtBAmDTZolLVoLcjCSemZZzfaEOsHfYKGe220OaJ8iShunj0ul2ajYHL+p1xX7kE17i5ujRN\nHfxQQ78zKyYjZAFlGpGXxD0i9UXZ6/ZZU5X6VmOHvtHIjMAl04PJxdIOZRBfe+21PNHemSdOUfLl\nc2ZhbU2IXqFc5uhx2Qz9foSv4cSl0OSRE5k9yJf4oNDpdDh//jwAn/70b+Jr5FUYhhzXMOM4iZmZ\nEaIdRaep78tm3t3bo9MRgvK3/tbPceaMHLjveOYZvv/3ikp74AMyCozjMHCGKABJR+anYzPCoizm\nYdPJrXy2MuMw4HKMA3tLwhR87t/+KoWytOedv+P7WDjxmHwvg7tSpN6D/5QXFFhtyphNBz4ljTot\nTk9zaU/GdX6/y7MnxKy2V5xgvSnXG0lKqAQnmChRnpK+ZJ5LyCA6JTsIeB5KWDvsm+Ek6aHw7AGX\nlRpDpsn4gqBKZVL8tnbrG0zFo/d3vbVOlGmKkFaUE27rWgn5AaaSSRqbQrQDG1CZEVX79s42U7Oy\n1t5/9tsorMi879V32VUmIt5s8ua2pIJIahlPPP0OAHpZjwn1vbp28RITpyW0uXpmgUYiBM34AamG\nMJvQo6KfSYX0AAAgAElEQVQ+Nnv1DgWvPHIf0zS9qQl7ONzeWpt/dhwnT13woQ99iO/8zu8E4OzZ\nszmTNRy11+12c2YKyFMgNJvNXMiJ45jHHpM1PDMzk69/3/cPhEZjcgZqEE02CkpBxu62jM1+OyQ4\nKQf+3JGYKJD3lfYcUnWtmCmHFCqylg0O8Z7sXbfqElSU3iV99rVLE46L1eiwloVsQpMmFzKu1pcA\nmPYmqZalDTMTJbK2MmXuHDPTaqotZFQ0rY2TRmTp6CawfrtDFOk6TRLqW9cAuH7hJZyWrLXpgs+E\n7rnqRJG+pvnopwmhr0xTluBPqh9N6uHpfDrWIVS/s/7ONp/4578KQIoL6nqQZVkekbm7s02vLr9b\nqE3gWmlbt7VLpm1wDRTc0WnVsDkPcxCRnOWnkLosmEE6hz79hiRiLQYFXluWBLoLx+d4/DFJjLm9\ntUlLzXnGOUj14LuWMEjyfnnqahP4Lqn6WEXYm/pqCXEa9jEcfa1GhQKBpm3wsxaBlUVWr6X021af\n8ZlQE2Oh1WZ9Rkyx1jtOpSNju5ttkarpdrrhsPKarKVrvYCrbXUbquyyqG4UU9TJUAHitc8z+T5V\nHMw9jnVUYKYHKjQ0Grv0dU4Dz+Ikd6NoGGOMMcYYY4wxxhjjLeO+aqaq5So95YQnJyYJyqItur62\nlkuQ0xNV2mpi6XdbuYR3+fp1NveFU20lPTwt6xFWiwTKzXoG6tvyvO96PPWkJNQjsixdXpJ3Jhn1\nlkiWzU6LSVXBO46bSwf9foSn0qrn+nmOjvsBO2Sa2tgSDdqXv/JldjUKwQ09djSaoVgusq9iZLtd\np9sTybTdTTj32msA9HpdZmdFxW7cDucvf1be+fIbudP3D/3A9+Umvzs5oweuoaim14kwoK3SzPr6\nPiVVr1cKhTwZqpVO5d8fLpcyiNQ03W2uv/oZAM699EKe2+OJ9/xOXHWozOKIm9vrDhwzRYY6UCPa\nr/kwGp6fmsc9qqa61g7L2v79ao2rbVmbn1m6SkPLw8Qb+1xbEy3MTq+Hq+bWWT+lviGSU4rh3VNi\niiilsOccRJnlUrJx8jXo+Q4FTVa5t79PS9fs4qlF+l1NtkmXiaL0t9NskrQ2Ru7jueVXcNTk1I16\nZJlIZjEJBUS9Xq1XSa7L2j+6sMjUvDiHZjYjUDV6NTzOoif9xViWdi4DsLFyHWral7BIS81YqZ+S\n7Er7s06X6SfE6XyPNp5qql23kGvH3IJHqaClIfbr7O/vjNzH2zmgDzC83qenp/mDf/APAvAd3/Ed\nTE5O5s8MJ+EcvKvZbOaaqTAM82SejuPk0WKdTocrV8SsFUVRbi6s1WrMzIgZZmJiIi8hczeaqe61\nBjPV0wBcD3bYa70pv99vkSWydmwjy8t+bNDD31an25YhjWRcbWYpTckzxSCg01At1W5Kz5H57zkx\nVp16/aJHNxVNbDAzz3u++RkZpyzmmiamPbd0gd2+jEGlUuSIUQvA3h6RJsQdBS984d/kJtA4jkk6\n8rvHFmrMTLwXAM/18JWW2SSR8ktAL4noDyJTrcHVAIoo6ueRfeCRpgea4YEGqp/0SdI8hA9fSx1N\nVQMmS9KXMAzJNHNRN4oZJC5yMXhmdI2GEKivDZax5iCS8RAcF5C1WW+6TM9/EwDFygRNDQZotiP6\ngVoHggxPNb2h71EoDKKBHfqZRpfi0NVyaob0gNIOt4eDABlRXo1OWBubDYJZTSgauiQ9WYdu5Qi1\nRdnrT7vH0LyezBe32S1qIMFkQKkr/Z0J94g8scYUy2X2fMlflvUD4mxwtkRYoyb04iQg/ep0EoKO\nRuknBpdBcs5WHlTV3mtg1PpQOzKJuQsW6b4yU6FfpFCWDRxUarz8upiu3rx4gTOPntZnLL2WRJyQ\n9GipPXt74zqNnnQ+dg1pIJ3PvIR2R4nb/j62LYv76OQ0rabMzMtfeomoLaq7cnUiT/JpLHkIa7lS\nyhmAOIpwlLlwXHOXYa73hgFx39hc53MvfA6QCJ/3vk8Ix4svvsi1ZfEFO33mNHMLYuY5d+4VriyJ\nCvyLX36dvhKCqek5nnhONtuzT7+HT/2G1Ch85Ow8L74i6uEzp4/z/m95/0jtW6i6BJrIMXAMaai+\nTgk09jWLc6lPuaRZscMg54FsZnPtsWMcXCV6za0V1q/JIWw8j0D9jwqFKoaBP5EDh3wI9J321tkT\nbsySPCqOhg6leWlD3+/Q2ZF1F3b36GuUzn6/xStrompPKbKvph9rU6zWkosCl9Rqos5exJyOiWOH\nfWRiKlMiVIRBOLBnEpASqDm3023Q1/VrowTXyneb9SXa6jNlsxZJf3vkPrbiXYwm+fTCAgMHrV67\nRVd9S+oru9QiIWK1mSnWtyQaZ2piMmfu169vcGFZ9vFOa521qxI5Vj1W49TzpwHYjZvYghDzgl9g\nd08yaaeBpeXpQZmmFAoy5o4Jcl+8ftynpQefH7i02wPV/J0xPT09EjM1eOZ7vud7+OhHPyptcJz8\nYAXy+UrTNBc8KpVKzvwkSUJBmb5KpXLo99ptWT/Xrl3LGSggf35hYSEXGqMoGjn9w2Rpiq2++KsV\nj8TU1JcusoZkW5OwblZgQtbj9PFJgm2Zh07Yo6++aOG+l2dMP/XILJ016dOVCxt4VXmPv2NoadLL\nXq2F8WXM3rh0mUzTczx1dpEjp9TfZ8Lj8rrs3f1um8iRsVxb2eEgdeKdsbN5eSgjtyVRwbY8U2ZC\no2Y9E7CqwnLSbhGqudUxlmNzIgBIEOBAgCFnpvoHWQYOMdZRHOdzniQJjprJtta3qBRlH5cqFTrx\ngcnSqBnftQ6uHT2Fh7H2IMEmB1TO2CynnWbof9eB8ozsy+0dCKbFVN41RdKGtOfk8dOst+WZRpLg\naNqfRqON0TQrUd/SUn83r1RgkCvWpAdJVqyxQ46uwxnZ7w42DSnPSBT9bmeNpCvMztFOmccf0ZqW\nTosXmtKeveIUXk3oTTu9RO+aCCpHFmZoGVGA2OQEV9C6hPM1op7QnkkCuuqbfWmvjhvJO/sdy3uv\na6qao0/TL4kgl9o3cprXa3dxlMZHU2Uam3sj93Fs5htjjDHGGGOMMca4B9xXzdRkbYKCJsTaqfd4\n4w1RS8c2ZVbzS8RJm+0tSSlf8CyhaqBOHpul3BJJbqfbpKP1haJemkvM3VaXWc0Bsr6+Qb8umqlu\ns8PsjLx/4chirgY+eeI4fTWNkVkKWjfQ83yK6sjuOy5RNLrq/a1guLTFyopw16++9gpzc2KeW1hY\nyBOqra6u5lFGV69eZVfLkly6uMTlS9f1jQZfoxonZmd585Jofd58/QpTmvtnb3+Lp54UZ8XPfPoz\nPPfO54A7O6PXCinGajLJKMmltyzL8uRR7WafqCsS5GStQlkDAVJnSG1tk1xN3G5usrkr2sjMHiiZ\n+3EPV3MMHXZAPzDtOcbmjuZpdhAhKFGYt+3KLRFFHZKOSDDl6RpTLXUsbe+TqHTuF8ugTrWu8QhK\nsja7nT6OXg+nJ0Ed2Z24m0c7ps6wpsQSq4NtmqR47qCkQ0ysSQlrkzXCQN6fGY+gVNUhrJFOn9S+\nt3CL1ZH76PgxyaBsUz+iqk72Jddjvyv7JmnHhKqFbEd9lnVtZv04Lz2xXd/N87zt7qyzvyEaq7Pf\n8ggFNR1N9AJma9L+0A3YUAfS8ok5kqo8U3FSemoaS+IDc0KlFOC4ofYxplweJIG5M1zXvWVetcF1\nYwzvfKeUW/nIRz6S53vq9Xq503mWZbkTdBRFtFoyPnEc59qrnZ2dvNZeEAT5dx3HyTUevV6PzU2h\nVWmaMj8vGoW5ubncpNjr9XLH9Dth3W/Sn1VH990iR6e05t1OAfe6aG0mkhrhtJgZn5t9hEJVrl/J\nrrG8sSRtWU7YyjRHlWvwtGxQuxRS1rIxFCzhlGg3SrPQui77fvd6xgs7kuts+dJVntGIzCcerVFe\nFK35Zy/v09JI7JKbkIdQj4BycfIQfeyrhiu2EYkmlE36GR3VLByZnKaiptSLVy7n9KA0N4WjuQwN\nkOSlUw4iutMsyx21HdfktS6dxJKmQgNWNq7xyAmJiK6WJvC1lE5qU1ALhs2MJqIeEVl2UJ/THPh+\nm+E0vEOR0MYkOJ7mz8pSukp3Pd/F1b3ohUVsR7VjTppr+JPYgCM03pqMOBuUoyLXvpEkB/kDOcjX\nZ2+se3oXSqqNVgOUfhjjcVqDSkrNPa5tShvC4jrOjLjmbDVCErUIeGdTZq9qPsjik9iuljKKWnin\nZa6PPDZNeFbmoneuxeIjkvzz2KPHIZJx2FuuM1CKzgYuW0rzeq1enuDUDQx7W3KmJiSsvbk6ch/v\nKzOVWcvKVfENuHB1lYkJ2XgLxxeoTciBu7/TYL8uB5lPRDGUgZibreEW9ZDd7tLXYsV+ocSsJoTr\nFNoUNNKp321SDGXRnHnkDBU1sdQmJsgG/ipZxraq11vtTm6amp9dINPM6JVSmVJxdAL+VjAgttvb\n27zwBUlENz03nZt53njjjTzaZ2tzi8kpGbe1tTW+8IUvAHD1yhY2U/NDNaSk4aCL81XOnTsHwOzM\nDJWaLL5r167TbcnhVQ0DXlMfq/e+9713aKxzUEDTMfT1YHQMefQLVjIFA/Q6PQbqaS/wCTTkPXMg\n7YuKNu7tUaxIu4y/R7MrhLHdaYGG0QthGfhhpXkBytBzcAf27l5KrOpsQ3bI/HdXFr8+xE1hsp3a\nLFNlNV3sNfIs+UFYxmgqDddm2EGEkrUUK7KOJqZn8NqyGb0sxR0kI3XII03dwMNXX44sO6gPHDhu\nbupyHI9M+aTUOPTVPBdUTxLWZC1EtoXnHhu5i8ZImgsA1wkw6m8QhkWOHJMf22/u0t2SNXJl6TJ7\nWiw86Ud5nFFYDsjURFgqVfM1GxQCYhV4fNfia/2+frtHqnu3/MQxbEHmt2JjGp48s7/Xp6dCTqU0\nhacRfFnqaVHS0WCHszrf4vrJkyf5ru/6LkCKGA98nZIkyf2ksizLixi3222Wl8UvaGVlhf39/fz6\nAIOIQBBmavBbruvm+3iQ3gTEHDkQnK5cuZILKHdC27QoH9Ws9NMlqkpPjzuLfPpFSVh87coGp1MR\nrC4XDYFRRjnNmOyLgOkupFQ08q7XjUk1cuqpp+bxA5nD7VafbELWe6ffw25oVKI1WFfW8vJal80V\n8Wnr7EBYljWydcXhiL6/7MWExdGzSneJ6GtEt7UWV6NaE5vhKQ2I2x1OH5WUBo+eOIFRgWRu4Sgv\nv6Zm8G5CqOdBJ41JBkKXd5AO0zkIMMZ1IY3kerFQodPWtAq9DFfNvJ1eD6sMlzUOKZqQ1WZEvdHN\n0dZmOYGS8qLK9NuhLOxpRpYOUoRAwWrk6NZ1ljdEiD77xJM5vXS8MPeV8wykum8y62FRwYw038ed\nfsQgPYM5ZKK02HRQ4+/Q1bvKgJ6mlu1toYXl+RJzT8v14pbDy01p51zb4NaE4cKZJ9pTM+52nblJ\naUNju4+1Qp96k21mzipjOOEwc0KEk8UTT7Og1Sm6j6ZMZlpQfKnO8hvy/t7OJlOPyN65HMdEPU1O\nm6WkHRkrW4qx0ej0ZmzmG2OMMcYYY4wxxrgH3FfNVLfV5TOfFqfq1y8vUVFnTL8YMDkp0k2hEDCr\nOWw6jW0idQZz+h4VzT80mUzQ2ldO26TUqursjAuqApw+UmVGTX772zusaALPtfV1XI3+e+2Nc7ia\nY6RcqeYmrrmZOQp6/ZFTj/Cudz57z30fTrx5+HrGxYviRPrZz36W9XXR3O3Vd2i0xHl5v75/YGZI\nEja2RUOwu79HVUvFuF6d0BfJ9PTpE/T6qqrs7aKBjyzMTJCp42XUtry5KmMyO13h33/814E7a6Yc\nO1wfKyNUzWE5iGkPssFhSPWZXpqQqoTv93r0NKqkUCnQ2RDzw8Vzr9LQ8g6e5+Cq9qdbb+DrK4uu\nhbzuVEaloL8bmlx663ZiOjr/lWqQmxFvSI9yR0wcPUaqWpu4Ywn083ypSE1rOPWSKE+8WQvLea29\naqlMQU1yO7s7dNT5sZBarJZpSR1LpShrLfAcHJWwHQxOqrUikwQ8TZiZmVzsSbFYdcJ1HJ/MmdH3\nzObJPEdBt5PkET5Fv0BXtSFtOpQ1SalbK5DuS9+3NtZoaxRpu9ujOim/W8xC2oMooJKX5xrz/Ap7\nXdHa7Ne3sEXNM7SfQEvW8l6rTqmvEVZF96AGXznDL6hJxk3y8iPN1j7GGT3pW5qmh/JJDTujD8xq\nZ8+ezZ3Ce73eoeSZw07ng897e3tcvy6agJWVldxslyRJbiL0PC//3TiO898ddixPkiSPBMyyLP+u\n7/u5duxOWAjnKKrmaPJIyOz0+wCY8PvUv1XauFrd5ORzkszT7dRY+bLQmlZUZ1W1J8+dKvOtZ2V+\nqlFAR+vxrboeO5EmrvRj6ltCjxbSChN9eaZV7LCjmmQTBPR1/33lq7s0djQCbmGOqigKiLOMUnH0\nvHbdfit38nddNy/x4rvegeY2g4pqA53Ay10ljp84wTuekvxmq5vrZKod9QOfgbIlS+I8mMVxnDwP\nYpxkZIP6r5FlQ2sLBk5IuVLTvqQMUi2l1tLvyx/ZIXeDO0Oi5IbWtWr+rclIlRaSJCQ92U/WdXA0\n4jaub1LfFtoZP/YYBd03nnEwkWghHQuZU9FXdzGa3BKb4g1yckV1PF9NhGaGONX953Rw00FJL5dM\nNV9CVEfvY6XiMqDfE7V9rlmxCDmTz3Bq4gMAdHdeobUsmsTCieMEJXFY764usTSjOQzDOjNN4Q/W\nd3eozcvabtVb9B0JtHj2zFM8OS3JYy8tX6Sn0c9xP6PtyZjsX73O2VlRj81MT7K9oecrDp4jz5dK\n5TwCdBTcV2aqNFkRnSMS7r2lPk29KMnNMO967kkWT4rddO36NVpNXSiOR6BqWr+QkUSiJm80mnkU\nHokhsOqvUpkkKEoEQFhyKE7Ihu92O7mvyNbudr4eyu1Ormq9euUqBTVXVEtV/LtIwDaMYQJujMnN\nY61WK8+QvLm5wYULQuAuXb7E5UuSAbbeqOf+Ib7vH9QMq5Xx1J/rkccf5ft+8IcA+On/+m/gGtnk\n73nvR/jyl4Vpjdmkoz5fSxe2SZU49vv9POP76kqD//ApSZnwl//K7ftkGIp6MYa5WfnNjXpMVxk+\ni839HIZDveMMMrVgxL091t+QaMKvfuUl4kGosuMSqv+R020xqeslcMkLJad4hL6MR8FzsIOx8Tya\nG0Lwk34BTyOUCsUwZ1hGgS24hI76rbgurn6u7veYCdTnrB/haW272sQUPTVZGmvzenw7ts81NX0W\nvZCKmqCHw4r3r18nUR8lkphYfUu2tzY5/rgw8bNHj9Md+DEl8SGmwOjBV6rVMN7offS9IpGGrntE\ndLS+V+Qm+GqKKlRLhJPyztW1NbbW5JAq1yZz3xJrU6paDDSKO/lerDglMuWEnSlLtyeEdPnieWqa\nRNLF5EJLuVKi3RDGJPACUg1F7/f6VNSHKIp6eMHofUyS5JBv1GBNVqtVFhclkqfb7eZJOIMgyBmZ\nKIpy89zW1lae3mBnZydnoHZ3d3OGKI7jXBi7MSXDMDMwYJqSJMlNh51OJ3/e87xDUYS3wzOnnuLa\nNVnvnbV16o/8C/nNSXiH8FU88cwM1eOyZtvnYPsFOZAff09AqyO//8Vze2zsCnNZKmcsLsq6nj7p\n8NwxDWd3Mvp9ZV6igHWN5mvOVXn9itDia90OqAXP6XvQkgPfLfQoTwstrtSOs6qFkUdB1S+SqWnP\ncR3idJD41sGos9P0whFm1E3g+sYa5y6IL265VmVGfWVd3+P6pqTwMJklUEZJvDkO0pEM6mR244hU\n399vNrn8poTg16ancAa+VBmkg2oQ2c2F5VFgYMgP4SAZQmLd3PzuYalrstCy8cnUX2y66vDUGeFU\nk/YORv2hTFIg1SzsKS6FCREYXJNQKeqeyCJS3aP9NMNRn06bRjjad0ty4F5hDbljbHaQRHQUlAp9\nUPNh2G9Tvy7vfLnf5ORRMUnH/iR7M+KPVvFTspacc9OcpBXJuu1GDc4G0pdyr0DSEvo345TJVDgs\nF2dJfBU4vX3Smq63ckRpRvbfpY1dpleFQS5PzrBhrml/YyJl+tKsz91I4WMz3xhjjDHGGGOMMcY9\n4P46oAdunvhvenaWySnNjxGlfPmLwp3OTB/N63LttEOiVGsu7e6xd0k4yUZ7jy2t0N1o1fO6bq16\nF1ejK+amjhAqlx7HKYmWn4njlIZy+Elsc4l/d2+PsuYnmahWeeSkqBhPnjqZR1jdLYwxeT6TtbVV\nrl0T7ndzc5tmQ6ThXr+XSzTHF08xq45z9XqdppYo2d3dZWtLTCz7y+s0tI7QmUfOcOl1eefOxk5e\nJuAT/+7fsLkpzxt/Ly8nUY828/IQxpihyKUuaTY3Up8sh3P2+M4gUafPbnMQGZIdOE4ay4H/oo+n\nOWmyncu88uKXpF2NNhXVIuF7eINyLAGUy5pkzbqDyn84GfiqpXKNoa9jHPiGqZpIP81Gh0ZDrxcC\nSsXBUj/I83Mr7Nd3qKqDuEkdHC2fM+V7hKo5SvwCVa3hFAQFwoJI8J1mi15XJKFmyeecas1CJ+Qx\nV0vL4AxKTnLhpRfZeFOc/wskJJFoRvY3d9lZEjPst/7Q72eguOv3+jj6ziRJyGLNcwQj5ycCCJyA\nXiQakHbUJlEJOwhCQlVtB6GHqahTbZrQbIh0WJ2YyE2WGSnVivSrbIqsryxJ31f3qZ2R8Zk+UqOv\nTurNI9twXTQZtXIFV/dWFCeUC+plP1RWx624dFVTMzU3RRiOngwxSZJDmuE8MfD0NHt7ovHe39/P\nNU2+7+cO4nEc55qjl156iZdffln6m2X5+nccJ4+8a7fb+Xdd1821UUEQHHIoH5gXpXSQzds5wK2c\n5m+GpY3rrL+m0n48Q31ZnOBLR1qcOiPjNFkp02sIjSgtlHnXD6i9bTaip/v19Vad3T0Z47RQ4YoG\nXyyvNOm15f3VLB0EgUEpoTMh+/XKWpukIr81daRIMoiYbEV4FXXgt/Ca1l+bnvE4duqxkfoH4Lvh\ngYk7S/PkillqKasJbHZ6jm5X+r7bbZHo3lrf2mJuTs4Y13EJtMaqH4Z5ZF9mxG0ApBzQwAJQbbXZ\nqct6/+prb9JSWvzo42dJGWjIM2INKomTONcuZVk28hwC2DQ9VLNs4BTuGIMXCa3v76/z5Amh0e94\nfJH5SS35NDFBVzvcS10K6sIyNwH9jmqV6106jUGi1D59T/OF1evE8nqSpIdRrW9qOmR6XuKmucvD\ncLkra+1dJe0se3OsrYpmsF23LM7KXl/ebrC7JxaKdz/7Acp69vfbuzh6JgXFgHhNg8NMgcefkz3X\nrHZJtM7u4uJZTj0mUbmzx04RIWtvJV4iSjX6loiJVN65eKZDtCpzeip4irovPMH1nRUitd406ykm\nHZ3e3FdmKorivNjk2Ucf5dgJqRP30kuv8eaamLc+8x9foKBh/fXWHon6payurbC1rWpaJ6GvRTd7\n/Q6umhY6jR6ORiqYJKTgD8Idfax+3t3bo9EQQlouBpSrSvAnazz2qLTn+Xe+i6eeEFPj1OT0XW0M\n4FAag69+9av6+Tp99S1JYnPIH2Pw/DBRNcalXBTbcDBfZaIqRKHZbuURRFcvXePKZTlwDR5JIgfx\nTrxMMChenPqkan7L0jg/BF3XJdKEc8aJKZdHC8fOzIHpzkDOyE5PFdhoyu+3+xlOfjg7eErEor6h\nptFEF89/maurcoiFnkuq/jWBUwFto+d7DPhYa00ewWex+e9aY7DKLIYOzJQ1xN8zrO1pe7qdXC0+\nCtzAwQuEgruGPMndcddQbWm9qKkCoUZGLq9cZ2ZGTV29GDQtRKMX0+CgBliih8JZY6gowfTThP6W\nCAm91jYDNbrTy9i9IuaKbqdFrGkP+kYiFQGsY/Lald29+K6YqdnpBaLuICoTUCKWOim+MsJRGtHv\nCFEKfZeCPhN6Tu7X4fiGWPfo9PQsRxclPP/cl87x0bMfASSC9sKm7O/5Y/NsXNMs5nGaR4D2+jHF\nwkHUrK+MfmotLdvT3wroJ6NH1wzX5suyLI+gdBwnzzg+OTl5iJkamOriOM5N8S+99BJLS0uAmOEG\nyTaLxeKh9w8i+nzfP6hBOUQ7Wq1WHukXhmG+32/lV3UnXF1eouzKunDaFXb2pe3Xlq5Qf1mTN877\nTC+o71J5jyMTWr+zb1hZlmeeO+7SU7/KJ48v4PvSv9c2V9jR5L/Xd1sEGtnk7Xr09qW9lzY8ggWN\nXj3apL+i5vcUslTGo+uUqG9IX1euXKI+eh5EekmWu0ckSZoX4fVw6LVkbV7tLJNpDT5TDJg/Libc\ntc11Fo9IiLxNM6Y12hFDXt80KBRIdI42NzbIlDmamZmlrD6Lr5kXOXZK1nVYK+YVOuIspa9mx34U\nUdSqAEEQHKLld4LNbJ45dJg9cdMW0aooGTauvM5Hv0my8/+eD7+PROvXul6YC1pxanPfzSxtUQkl\nxdDl/+8T7Ccanexl7F0Tf6Wt9VU6LVlv1imQ6J6rTE5SUj/kQrmMr36ikifBzT+auzkWC32UdGJc\nj1JFU59UDYG6CLY2V+lpTcnN7RbFgu7FckxLmszRiYyS9xUAivMhbk8iayf6l2H5VenXUkgUy1zP\n93aoWDlbenEpT4Fwam4Ke0RMz1NhlWpHhOxHzr6bzmnZDNVSmY9v/cbIXRyb+cYYY4wxxhhjjDHu\nAfdVM5XGcZ5T5+yZY8wfkbw47U6XsKJRQIFPUFRO2ItoNFVj4VsmqvJMnHRpqtoyS+M86sJ1Xeam\nRY13ZPYIkzXR5kzNz5H5wsm/+earVBvy/sWFec6cFonj1PHjnDktzm/HFhZzbQqZc1cRRADnz0t5\njV//9V/PtUiO49IbpKzvZLnDdZalB2WZLENSWEaqppE0S0likXQa9Qbr66Kh292qU/AHuncndwp2\n3QKafZYAACAASURBVIzUqvQUpWQqzVvS4YxwpIlI/K6XMa3lCe6EZjsiMAeaqUCd4Qu+4eiUaLda\nvZhSKOM3VS3muZl2N/fZvig5rz7z+S9w/qpITierIWXNoUK/m0eYVEvlPF5EnHrVLJJluXYsy8gT\n6nmOoaxRfqXQx1Upf323QTcaXVI0rktTzXkm7kNP56SbsGgHdc4cCsVBbbs2zZaamjtt+pFIgd1+\nHzTa8WpQpBvLeH/EGgqakPP48UX6xyUi5cqrq3lpCxyXmdpgbl3aqhWIsjT3AcWSJ890Heeu6roV\nghITFdFSNJo7dDTnmBs4xB3pu00Muzta+3FnN9dGdVotMk/NDPM1EjvIbeNz7MRpAK6dv8yrXxDz\n5dOlJ0hVS5GZlESl+TSKcVUDiHFwNE9Pp9Wio9ooPwwIQzH1p1lMa1BqagQMO6BnWZab5Ky1ueYg\nSRJWVyX/TbPZ5NQpMe8XCoX8GWvtoSi/gdYpjuM8yjaO41wzVSgU8t/yPO+QBqpe14i4hYXcpJQk\nSa7BCoIgT/55J0wGcyQaQbbWaJCqubVSPc7yRa0HuNrkxKysnXbZcD6RNrZbEY7SxKc+UKOwoJrP\nZg9XzRwLQcBKQ7RzM0EA/UHSywLrTRmPR599lrQsZtut7S1aFzTpZcGBqmoWIkumWlDTg+3tgxxb\nd0IvPRgbC6QDDV5msKo1a7Rb+DUZ78QFXwNY6s0m66qBPH3iZG5KazWaWLUSkNrcrEZiaWtAgZM4\nzB0VrdajZ86wtCGm0nbcpa0BC1Ga0FMNERa6epwWwjAvOTQKjD1wOjfWYpSOt7c3WP7SJ6S/RHQb\nQi83rr2JXxF6vbyyTkGDUBjKTVcuu6D0JmnusrOtbiKtfbZWxJl+c22V1IrZNwvmsYGsAT9cYWpS\n2n/s2EkWjotrTm16hswONPx3Z+ZrdPdJ1LJQCjPSgtItSiR6zi3vrVLU8kXtjkuzLjRpN+iRKW1Y\nXV/jC5+W/iamgNMXlVXgdLEa4JPElnYsmjXX8ygHGqkc+wTqHpIQUpqVhMelosv88XcB8H3f+V9Q\nqqppO4lYurQ0ch/vKzNFYpmqCWGcm51mUrNxP//8Uzz3TU8C0I3TPEJi+foy22oCWZgrs7M9KAba\nZ2FOVPbr62tYjXhYWDjB6ZMSElkoTOEajbaamaRrZJP00hMYK2rg+ZlpTh8Xhm52cipXsbfbHXxV\n2XrGv6tR6nZ7/PI//n8A+OznvsCjZ8U/IAhC2npgZRl5yKUxhsEZGEcx6SATLhmZJoJsNpt5yoSt\nzb08YWk4tGmb7X2IB6rijETVujYjj1j0fRdfberYjPkFIRa1WpUf+IEfHKl/vV6KRv5jnINMv6EL\np4/UBjfygwJraar5b/3Si/za//vPAXjh3BKpHlA2zEBt9CYy6D5j8egRPG9g9khIlSlM03TIb4X8\nt3xr84zpcZwwoX5S/vwUrc7o9cCSJMvV6H6S5Ix1Sp8jqvqfbXTZuLak7YlY10ihJOoTKzMVRxFB\nLOu0UXIJBsV7MzfPet5PEzrKHfUch726tLMVRcyelYPdcQMcHWffOLk/muM4mKEM385dpHy3cUao\n5hzPdQZRy6Rx9v+z92YxkiXZldgxe7vvEeERkRGR+1ZVWUtX9b5w6ZleyOGwuQwpSiRBiRJH5I8A\nisB8ciD+CdCHIEjACBpIkEBgRGgbUhQlgRoO2WT3cLqru9isztoyK5fIjIiM3cN3f5uZ6ePeZ+5R\nXVXpyZSK8/HOT3l6vXj+zJ4t1+6591yklsYwcNk43dnaQsoSBf3BGKu8qdV6VbgVprqMRshp7y++\n9DJefY0yRGVD4cUv0bzcmxxDsEM8EC6qnKFrfA+CedA81tjdIwOnvXYGkqmxJM2gzfyT8b3xTQXN\np5SyBs5wOMTbb3M2UZbhwQOizV955RUrmbCxsYFbt4hyNcbYOec4jjWyZinC2Y3UcRxLC85m8Qoh\nsMhF1ot/F/cvYhkfh1DUsZ/S/VTTxQLLyzy6t4+xoc+T4w7WWhyrl7pIj8gY3Vitwl+g+RpUq1h+\nlsZa70jj8A3atOU4QWVA4zT0fPgpi2QGATJ+3vqCQtDg8IIHQJNrrtWWHAyqHF4wGiPQNI5yZbC8\nsTZX+wAgdFzoImPOaKtW7jgCPm/4nlKWqpPCJ1VLAHGu8JDjDjfW2kj5XSXJeOYQAhju77DiQaX0\neXy8hyNB16+utZFwPUxlDCoRywkkCbpsfKVJCqcQxlTaStDMAz2jgA4YGA4rEK6HT3/qFQDAjeev\noX2O3tH2wSHOODSmdvf2kHGYw73NLSs7UasKdHbo2W5//3v41uvUD5nRcFkGfDwaQjm0H+t6CFcQ\n7eXkPcRDGldH2z1c6dF+eeMTL8MNZuIaxROsN9pBi7Yb5GkNeXFARQDNMV81N8QgLqpE+BBVPiQH\nLYB/Kx8Pbda46j5AktPaP9AVGD60Nz0DzUr5SgMpx1zmxkHOkbeDSY6TA6YIl88iCi/Sb+3cAw8r\npF0D0Z9/3yhpvhIlSpQoUaJEiafARyvaOYrh2wBLY6tUL9QCpOzoTHtDHJ+QS7I3OsZgRBZy6Bss\nLxZlGkIssFs3dIBKjazrlZVzEIIs835/YLOwZMXB/pA0csbZEHUOcM9NgozpJS/wrNfGAEjZDZwZ\njfwJ9HvefvsdbG/Tya7bHeHb3yYRso31Cwi49l8Qagg+GSdJYuu9UekJspy7Jz0c7x/bthSZQlIK\nSH6eJI2RFN87BjFrBWEmU8h1XXtSdl2BvGiv5+PGDRIt+9KXvoSf/MmfnKt9UTAVmXRdz9JwvudO\nvVGAFe08Ohmhc0x9f/f2q5ZSaddqCDlYPJIKWhfB5RJLK+R6Xjt71tazS1QOoYpg36m+C3mjplpC\nks8HruOiz5pBUjioBvMP9dbyGStMmqdj+KxpVVVtNPjkuv9ogJ09OtnE0kES82k1H9uMSWgFA/a+\nqQCyCN7UClkhuOUKuExrS9fB8ZhOk8fjCVShdSUENI8XRzj2ffq+B5d1mgwwt0cDAGqBtJmAcT2A\nU6N7ZlmOmHV9JkmMyjp5OFrrS9B9rl1ZrWLAtRRvHx5j8QxR63XfwQJ7O1tnVnDtOo2v7/zL78Jj\nr1OtUkW/S+M0uHsCn5OMKkuLCIqA03EfaYdPz63UzmNtFHx//gBtrbX1+Egpbf/MBoUPBgOb2SeE\nsDTc2toaNjbIa33hwgWbhTdbNmZWFHTWSxyGoaVclVLWM1Wv1y3t3+l0rCerXq9bD1eSJJY6fBw6\nR4cQ/MpXry3AYe/uzu4+GgE972ZXIb1DnXy+InCDnWFXrlbRZ6+jv7iMUY1CIobjPYzAmmn9CWrc\nV616hJzFOQ87J1hir1q1coJqk+ZZe8lB1mYPQoO8tABQySREkYSiEpuNPA+qXmCDwrXWyArhW5Nj\nwp4g13XhsTfH5AYj9kQMen0cbxI9d/HSGs6xttj+9hZcTnxwpbRU/Hgywf4uiZ22owpcHncwwr7b\nXGnUeJ2LPM9qoE3iBDqfekHVEyQtaaNseIcxlnREnme4+jGqmfqjX/ocDGcMh54Ljz32X/jCEja5\nja9+67uWMtt8eBd6Qs9z6/a7GPWoz8NaCwl7hYzyp+VwpAPF70hqHzqldvVGQ+xtU59cevYKapzB\nbIw+lYH4OLQXfKDJyWR91+5/nmcgXBb0TR04PH6WWgKVmLxgGhOA16SVroPLFRpXy+vG1s3NzcjO\ndd/R02gWCXjF/i0UJHu4kkwi5tCPkcqxf0Dz/nf/p9+3YSypdnFnr6h3+3h8pMZUvd6EG9DCElYj\nBMznaGEw4o2v0zvCwy0Wrhz1scdcta9TLLE4YBxP4DL3ubTQhOLCnCdHR4iTIiU/RJvTLx1PoM/Z\nYqO4D4+LJ8dKIuANPckSeDyYNIAJxwSEfgSI+fnvmzffwP37VFi4Wq1iMKQXdv/eLqqVIi06p1Ra\nUEaQ6xbptSn6fXqpg34PhqkmonVcvmYIxQaRUgqiqDOYc1YIaJP1/cg+k6U0RhNrrC0shHYjuHDh\nwtwUke85litXSsO1hqaYqX9nbDHk0HXRO9gEABzv71AMEoDFigfJm7nIcltHyokiXP/YZwEArcVl\naDZM4kkGfnQ4nmOvN8Ygz2aNUXb3OxJVNpqVUhhyVto8kEYh4jT9QZZAF5kz8BFy/Nz5KMUjVnw/\nSAwUP0Ouk2mcFwyUpL/1jQenkIgQGkWgnAEVGgYo3Tuo0CJZcyUk1zyDpPEJkDCpwwugkBK5msbY\nPYki8Xg4sbUZpedBMM9Xq9WxsEAb5XA4sIVcLz1zEbe/TdkyVy5fwNZtisnpdA+xtUvjvZuOcPEK\nKU4v19u4yPFTJwcHePc1ohmev3EN/RMySPpv3IW4z1RstYIzbTLEZBKjzSnPoetD+yyD4fpPlFn7\nXmPKCt/6vo1pGg6nCttRFFlqLwxDm/HXbDbx8ssUU7G9vW3vE8exNa5mpRGklKdisooi7jdu3LCC\nnLNCnUmS2L9VStmagI+DEB4ePSRD8MVrl+E5tGG2Wi78vKDzHdzZJmPqIPAAn9ag9gnAtiuO7u2g\nscDUZVXA57T7WuqixkWmx5nEPc6OjY2Py1ykWTljhBXqs94qcHRIz9DzYzRY5DNEFdssF+JFvi2u\nOw+U0jYmUsxkEmdqGq/mBz58NqbSwRgZHypXWguocTafEMAhxw2NxmOAjQ5tBBSvoZ1eF0ccjxq2\nV7C0Tn+7NzzBiO/phxGcmSLGdgMPQ6RF7Ks29n3O18bcZplRXjYbO8kYmUuOgtTEaPosCuuGtu1R\n4GHzLsXobr7zFg45xvHezi4E7xmd3hhRhWPKsgxJwvPJ8e36CpNAaS7KnicwbM9rKTEc0jsdD4eo\nLbJha6axvvNgMGlBcVtq1QqqIT1nQ3joaT5cZVVEEe+FMVDr0zhc+tgGVEj9+XEMsKHocF5raCsu\nCmGsirxjDFWTp+6E63NGtTCQsjjkCKS8lkwOOvCaRKHe9SoQvDdXanWMH745dxtLmq9EiRIlSpQo\nUeIp8NGWk6lFiAR5C6QvMGbrvTsaYPeYtF564z4mXFPI9QH2AKIiPCiuQp9lEyw0KVhuPBzhwV06\n9aZKosZ6TK7fwJtcMXyMMZKIrOsglKiyEOEoAfpMq9SrNfTZAq/4GpU6118apzg8oeyTK5ceL2xJ\nJ1I+6ZzsY8BaKNBNK6d//ZkrqLO+VRzH6LE4XBzHcGQR0Dqx/aCUslW/M92DZO9IpVpBlhZaQd6M\nRo1BxtliSZJaGnFtfRV11g959OiRPeUXJ+d5IISA5pOi1jmUmgaCTwNvBYoKP539h/irb5JWx713\nbkNxRqP0JIQ9oU5LMaxfuI5P/NCXAFBWTMa1GR1hcMSB7JNYY22ZPEeVQNiTqyMlXPYazNJecZZj\nvzulZx6Hw83bgGRKNk0RcP9NtAfDrv9FV+I5zjLbjSrYdTlZYJwj5euF0KcCxIs2OpCIOAtTaw8J\n18pya6u4sn4RAFBbXMK1a+QN8cIKKkW2l+MiiqZeR1PoWEkD151fSyvOJAxXnoeWAAqK2EHMOlmO\nDFBlt/7K6jJeZYoq8D0ILify3A+/iP6YS5r0DQoZqP3tXbicgToajDFhD0693kTEQbKV1UU0Nsir\noYTG8JDmmaMVzrPm28QTGHANOa9aQZ7NH9hbq9WwukqU8ZUrV2yW3O7u7qnMvsKL0Gg0cP48Zfgc\nHx/jnXco62l9fR0vvUSCgGfPnrXekSRJsLtLlP67775rvbtSSjsXrl27hhdfpLJATa6jCVCw+xF7\nSm7evGnv+SSinRevXUXEFNvSwnlkKQXJr5/xsXmL1jU/cOEb+t2jYY6vv0vXH45yfPZFmvfnAhd6\nZxMAoM810WStsMD0Ifr0rm6+vofKlQ1uX46H23T/lhNio3IOAJCut9HtkgaQ1wPClMbXyXGCSoUz\n6VIHy/VpPzwOSRyfcoBMOLkD0ti5DkNlwgDg4OEDq+X14vPPoj+k5xwmE2zvctbmSddqJI2yzHqm\nbr79FkLmTZfqNRwdUaaYcKV9t2meQerpE9nMzjRFzF9nWfZEnimjjc1MZBU9uk+e49XvUH8+c2kR\nQUhzMXAMFHuy3n7nXfzhH/0fAIBbd+8gz4sMUY0hZ8JrCAQejfc4GcMvxDlVbkMSTG7IowNAmgkE\nutx2YMRexYPDDtrnpnTkk3iJu855RFy+aG3lWVQjeobPrK7gXvev6Tn1AiJJY0MECgd7NJ6byToq\nC0TRxtEE9/fJE9e96Vo2hBIKuF1GWO895LSuqTTSJr/AKOuNP19x0Lh+EQDw+eVPwOO+NdrB3Tt/\nPXcbP1JjKs4V3JBd5GmKEx7oh71D7O4TNzlKxhBM4Q1HQ4SVQoQztq7N/qCLt98k99vhfge836Lb\nj7HQJgMkqLagmGpyGi66PVq4rq9dhmBaLc1TdFgN/czKCnyObUjyDHGPF3bhQz+B/669tGQNmTiJ\n0R/RJK8GLZw5SxvHz//yZyCYl5XSQTyhl1qvNPDNb/wrAMCf/ss/Q/eEjDvHkdDsd5Uig2bD6uqV\n53B0VKikj5ClRdHKGBmnxTabTfzIj5J44q//+j9EHNNi9Nu//ds2m+jy5ctzt0+SwAIAknIoNgHP\n8yyFLhxpM+/u3rqJzXsUW5RkKQrVizTPbR/AMXAiWgB/5Cd+Dmcvkss1V5mNlxAAQrbQ9ntdeC79\n2OWzKzBg6Qej4MiiQC4wnBQZW2OkyfzUQh7UYBpEh6jBEHGHqBQtpKU4o3GMRZc2i1azgUGFnk2l\nQ8vXG6HgcGaR6/lwWCjQCAmP4+eWz17Ep79CC8i410dtkT43Fpfgce2/SehhiQsFe9KBUwha5jmE\nUwjqSasmPg8qjRbqfKjoDEaYcP90R337TputFrygyIito7rAtKk06PPmdW6phpc+TePHTSqIYjrM\n3HnjTeydcNHubgd3b1H9yY2NFbh8Qnrz5lv4wtkvAgCuvfgcemMujNw9RtKi/swcDYepe5XmCJ9A\n1PLXfu3XsMJ0lOd5Vnjz5OTEbo6zhYUXFxctxfa9733Pxje99NJL1iC6ePGivX40GlkDrd/v2zp9\nruvi6tWrAIAvfOELpzIKC4qwqAcIEKVYHGj6/f7cgo/C1zh7kcaLMbldB50gR3dcFLnN4BUGt2PQ\n57inv76nsNWhNfeTDxfxSY/jcVYUFlfIaNq5e4jdW9TWvO5D8vt/8+Yu3r1JffPMpxdw4QIZoOcr\nLRw2WbKkI3DU40NXW8PhDfzwXY32ZP7afEEYWsMkSRIoltVwpHPqoDJiBfRUJchYzb3aqmEIphdd\nB1Gba7U2asiZWg/8CJVFmuvR0S5ufZ+o7NW1ZdRtLGuAjCnxzJFWMDjLMmtQKK0BrnVqtMbEKo4/\nHsNe35a5cxwXgmO4Op0O7jwg0eePXVlBrUpjbf18FV0uOt7rdvDpz1Ahxjt3tvBol/azOEmhmI4M\nowpiro0ppEazzqLYvcm0IgYEirKXUg8hnKFte8SFz2uNBegZo9/o+Xm+7DjAhqY+X40GaLi09xzf\nfoDFJhnv/uIy3JDaqLXBFhu/uzt7uFIjynVr4qHFvzseCCtYqqUsamBDZwYOCww7noFmUWHHlRBs\nKAmlYdjwdLVAlQ/5FTmB5jGGyQQvvLg+dxtLmq9EiRIlSpQoUeIp8JF6prb2dqHYtZaZFGOuQ9af\nDBAX2jwqRcaZDUJKq2UxyRO0GnR6arurGPTpJHK0f4IKe68WFs/g6rN0gjx74SqiGlm5naSDb73z\n5wCoMviYT22B62HA2jkPdx4gYGrHEyGgiswrD44Nsn7msW28du0aXnqJgnAPji/im39JVEEYtTEY\nkfX7z//5txGw2OXS4hIuXKBTaUc/wtuvk+bNuDtCrUpUVpwMIR2yqNuLy7YWWq/TgWTtotFwPPUS\n+S6++Hd+GADwG7/xG/jqV38MABBFoT0lP//885YuKjKV5oEjBbIZ926hAyWlYwUnpc7x7ltEsW5t\n3oONVNSAx5W9A8+By7a89Dz8yNd+EQDwmS9+2Xq1ZgOIHeliiYNk3cBHb0jvsD+cIGDhRwGJmL1z\nEAqpza4RWGkVmaCPh15cxWSBTrFx2EfI5RRc6aHCXke9uw+PaazIKPgsvuUYCYczhRzfQxBMg0aL\nbE4lYCuc15eXUVte4vYqFK5qxw+RqeLUmyJkb44DMfV0CGk/G5iZBIDHoz8eImSarxrVkXEm2CQe\nIeTn1ErgoEMe2sDJsXqFTmkjkcKtUJ9sb20jr5MnoCIaGN6j4ND6ah29ffaOrtYQbFJ7/8Uf/ymi\nViG6p/Cv/u+vAwDub9/Hsz9KVFrl3CIyl0tBGYUae4w9x7V11ObB+vq69WZ3Oh3bV77vWxquXq/b\nay5evGg9Uzs7O9brEASBpQU9z7M00myg+cWLF3H37l3720UmoDEGQx6rlUrFCu4+fPjQ0vKVSsV6\nrJaWlk7RuB+Go8MDrKzQOBWpgepxUstCE+c2iBKqDidoL1N4wrt7x3jjFoVENFpVZKyl9wd/socB\nix1/7fMvI+Vx9+b9Y3z/Jq1ZwYqPS2u0nj7qDRE1OPtzBNx9hzwIV841sOyTl0E1U6TrrPXjdLF1\nn4VAF1axKOenwLQ21gPl+T6MY3mdU6V8cvY4eJUITlEvTxiMDHuX8hwRe1n9MIJh1v94PEJ/QNev\nXjqHzS1KeHrr4T3UW+SRqYc1ZAP6g9qZFbi8dg8GA5uAUKlU4bHO0WQyAcL53iFAnthi8jqOgyrv\nZwf7j7B/fxMAcOev38Tzl68AAEyqsbNN3n7Xd7G8tMyfAyRMg6e5gleMWT9Af0DvQkCj3aZ2eV6A\nh/uFp8/AYaFnqQaocHkxKaton2FdxtW1U8LAYn5HOILdd+CFtM84k3uoBuRZe+PNPZx99uMAgI3V\nRQjeg+NRHwdM+9862Idmj1UwTtDgoPNG1cUZHqvRyhko1pHTt+7Bvcb1H7MY3h6tSXj+RThMZ++d\nnKDL3v5Yp0gUzYsL55+F4bAF3UtwqVGbu40fqTH11v07SPOizpawrl+F3BZazY1BUtSSM7CDw/MD\nROxSr1aBnFMn8kRgwPEwCwsr2OAMjIVWAzF3dH/QxcY6CcUZodAv0pCzGI+YXpyMxwjZ1V0J6gh9\nTgHNxVQ1fA60221cv34dAPBLn3gFC9XfAwB88y++g6pD8RtHewKuNxUF3eMKoq9+6xsYdrkIbKOO\n/pjaNRglWFggw+pzn/ssFhdoUH7961/H4QG5dR0X+PwXPg8A+JVf+WX8vZ/4in0eY7PvpqncFy9e\nnLtNs5ilKkixfXrvIl335HAfd974HgBgf+chRix1cWVjDX6F2rG5ed9+/vwXfwx/9yf/bWqHF9gY\nL8dx7G8JYSzlFwUSWtDf7ndHUJxV57kSSw2ajH4grPSG7/tWGXgeGORQRfaO58Gp0aQLvApcdt8b\nAUgOvKi606w6Y2ALEfueZzl6Y8z0nTsSMSusD7o9GKa1M61sdornVxFyXF0ACTkVrrfpvVIK5Gx8\n6Sc0pobxwGaCOnAh2QD03BABG4Z5lqPLcYR+mqG2Rq55HTgImjRXjh91sHydFueh7uLuA5pPNy5+\nHLWIlcsnQyywAXV4OMCIqe+zG6vwM6ZMjntweUPUeWILQSONoQ1nIpnpBjoP3nrrLWscVSqVU5tv\nvV63n4sNYmNjw17v+76VNKhWq5byy7LM/m0hbVBcX9BRo9HIinP2ej0re2CMsQaXUspmDrqua2m/\nVqt1SszzwzDoD7DCcjFxojBkOQnpS7zyHFGvo3t34EW0Ya4s+zg/oney0IrQm1B/HEQVZCFTckJg\n3KOxufcowaNjGlSXLznQPlMkkcKN87TOXlxbwZiJ/796NMbWQ84gOzhBJLk2X99HesCxhgsJ2k+w\n6/QnKXLeMzRyKxGiYRCyUZsNJ8gSFuuFZwvWD0cZxqwQX2tWUIQlHZ4c25qWaS7h8xyK3ADXLhI9\nOzjpIOHxmCVDjDjjrCqUzR6OZiQwBIw9M0opEVWnY+Nx0Dq20ghaSwyKLFtkaLDB3b2/hb/8w/8T\nADAyCl0O13CjEEOm6Af9oa2sIRzXrjcnvYE9ZHoyt7QXTDZzWFWAIiMC2QBS8dgMalhapb3T9V3k\nluaDDceZB0sw8A0LSbs+7g64OHPgodWm36pEoY1ZS50MfVbfPzjahs9GdCoEDriWX+gbrCzT92Fw\nhFxxnNd6CiPvcT8I+GzUO/kdiBbTtWOJ4Yj6oWdqWGMx5rw2wJCdPMFygjvs3JgHJc1XokSJEiVK\nlCjxFPhIPVMP9ndhmOar1kPUHKZAPAHJwcU6gRVwzHKNXp9ObNJNoYqiZImG4Sy25sISpCYL/P69\nTbx9m05+1559HtdvEN026J8AVbJgK9UIsslWbpYg5UDEhzsPrYx/FDRQq5L3RwoXvj9/N62stvHc\nc1Qa542bf4VL54jC+5PREXa3ORuq3kSdvWxZDDzaIwt8MN7GJKHsoPFohBEnWAmpcf0ZOmm224sY\nDujkGEURXn6ZMml+6md+Dj/9U18DAKzPlGt4r9bObNmKAk9y2tcQlLqHIgCTvvektCJud998HW9/\n79vUjiTHV36GKLznX/o4XK4j9fv/y/+IZpvc0z/xC/8uvEJ8UquZrESJgvYyYurKT/MUMWulHHdj\n9MZFQKuxZevOn6kXjwkpDZ6kvGLU3UdQiDMqDc115YwIkHfZU5OMbUZb6EnrLXL8AB5rmdRrdcji\npKWMpag8ITHq8DvvHKHNopeeIyHYU+NqA5c9bo4QNrgy1+rU+yok/lzXh/cE2XyeqwFZCCDG0Hz6\nT5MJJhyM2QgDtCL2UjkCC2cpmDvVBsdcc+HsmYtYXCRK6+S4gzyge+52HuLZF+mUv1C9gJMdcrVr\nAN0TGr95ksKvUP+sXdqw9NkwiW39syydIPdprhCtNj9F1O12LVU3WyNPKXVqHszWyCso742NNicA\n5wAAIABJREFUjVOaUA8fEv1TrVatd3c0Glnabn9/3wp+Zllmy8/EcXzqd4sMvna7bbNNqe6ksPec\nl+ZznRCGx0utsgDJtTFN5yE8znzO1dDWujy/UUWtTh6lZHiI9XPsLdxSqDe4zl3vAfpb7E3tCKy3\n6BkvrwtUuPzQD336x7FWIa/pvTvv4P4+0XyHnX2MOkV2m8K4z9m3HQdLKzSnY5PhOJmfct892EPK\nyTRKZfA5wSSDhj4mL74ZxXB5nvUGQ9S41Jh0XOzvUEaeyhYRcS3NNFdwOFjc9V0I9lqLTMNhYeBa\ntQ5V1DoNAzgcFD4cDq2GXxiEVoRaqek66wUBJtn8QfZpMkKRO+K5DgR74BebFSys8tqgDDbfpLqm\nQ6PgcmZorA0OmYIc9oZWs67aaGHCgf7jycTWr82Vxv17pBGXZQZwiS0RSKAyrp+Zp4gTDs5uVNFa\nJCpZmakXVwhal+ZFxQlsAD1GA+wnNDYW6ks4zyLNzYaLzOE90jkCHNZZhIPWOq1Ju2mMV1/jNcn1\n8dpOUdNyKnvlyDacQnNKSoAzpz0DKC6ZczJIkXCCmltbwDLbB34lg+EwoBwGd97amruNH6kx1Y2H\nlnqR2rG1jALPhdRFWiMgmMvPsxgxG01+4GFUiLelCkt1Fp9rruKbd78BAHi0s0t8F4Dbt97Gyhq9\nJFcAwwm7+hzuYNBgCJk3HQ1HSIt4mHQE7RRK2gHCJ+ymL37xRwAAcTzCn3/9NQDAOHUxYpezOZng\nhCUfdh/cQipo45jEPZi4oAQUFKfh+4HE0hIP6BlRv8uXr+Af/87vAACeefaG/X0zU5Dvg8Q4n8SA\nmsU4yWw2izIGmn3njWoFd96gtn73299G+yzx+z/zlb+HqzeovpQ01hzGL/zyf4Dtu5ThVa1E9nsB\n2FmR5bkt8KwwZeqMEdPCs1lqY7hyI/GQRQOrkYNljrGD0XiCUBvIcQ4h2WUMAcnj0ejUUpmoN6E4\ni3Ah7qMpaKEYOj6aDZrsF9bPYmmZxulB5wSjIS9WRsMJCzXxOmRAzxn4rhX/LDJNAMAIYY0CHU+s\nAn0GBzkvvL3OIXps6D334rOPbaMjYcdXv7tvDU8/CjBhysRzJCoc87WfxFAcm+ZA4vwLZNwvLbXB\noWnIszEqNa7dlSpkHK9SOVPB9c9SLGM6/p6tLRnWaqis06Zw+RPPYYOzSg8OOhjv0hh3XNf6zzOV\nP1GghjHGxjqtrKzYuTBb6DjLMmu8xHFsqb1Lly6dKlxcXK+1tpScUsoaYpcuXbJFkmcPKnEc298V\nQtjr6/W6/V3f961hJaU89fcfhvNnL6Di0T0mEGgukLHb3X2Abo/o1rWzSzCCNq60mkNx8diVBQ/X\nP0bG7uatd1Ct0G/GyRH8LvVZNszQ4koTVecMVloUM7q/P8H//H/9KQDg3t1tsJYyLiz6OLdAY3m/\nm2JHszRC2EXrChk4Z64u4ziZX96iEkoUtYSPjwaQHvXTMJ3Yjd3NDZyigkY8wQNeG/wopEMegPFg\ngv4JGbu1ehXwWAk7ndg6fZnS0w25EiLhf4k0R8iFdkUOGK8QkXXgg9aG4XAEny0iKfBERn/F8yD5\nwOblKTyel73RMcYJzYMjL0KDaWHfk5Cc6Ts8OEGvT/3Q6w5RnKd0nmDAqufCAIXisdYaEz6kaQDC\nob5y0YXhOE5dO4s0oLFUbTjwXXoBeZJBcGhDng1wcFgYGr/02DYm1RSKC6JfXFhE61laFztdA7FI\nz5kvpug79I72Tx5gnND3rusgX6TY5mrPxbVrFA+s61XUqxx64Ava6AF4MoBT1MkMa3Alx8pFTTSr\nLMWSxxARrVX3b38HUtCBoO5qMIuPwViitzn/xlHSfCVKlChRokSJEk+Bj9QzlUDZU6yrUsiU6zjB\nhcM0iYFjg/GSOIPk+mRexbU0hjAKrUWyMBejJjyvqCuWocX0Wbu9iOVlslrHemzLDYySoT3Zj0Zj\nKzUf1SIUFViUksi5BlSuNHT2ZDanz9keP/0zP43VVcpuyZSHb/wFUV+5lJCsSzRK+9DsfREqhHAK\nt/EEEFzGxJlqOI0mKTQHC1995iquXafTop6hf+i/fzPP0+Og8vxUNl/AdeveefN17GxS0N+Xv/Zz\nOH+VniusRDZgM1cpBD/X2XMXsHOPrj8+PESbyz6oPJ9mqJkpbWdgbLagIxw0q6w/5ghL83WHiXWR\nHw8maATFyR/21DgP/NULiDng1DgSgeSjikoQMO2ldQ73mCies3t97Hj0pMeui7PL1JbI85Gw63yS\nDK13yQAoXLRKSHQ5qN1xhT3Rau1gkd3rMAYpiwidHO8g4WyTXCsoLnX0aPsAm++S+/7n//2fe2wb\nlXJRVGLIjYLLelVhpQEvov/huwKaafmgsQC3yrUuZ7ydnu/BNdSW8+evoiXpxKndHEior7qjMRpn\nqU/WX7iMA6apa2fb+Mo/+Crdpx1g8wFRaZ4bodmguRunHUxi1rxxAkgRPLZtBT71qU+d8v4Unoyl\npSVL4c16gVzXtf9eW1s7pUVVZP8JIR7r1TXGzOX5nf3tWQHEecUQl6Iqvv19rjnqdPHx8zTnDvsJ\nVpao7yEVmit0Su8IiaDPnt5cQ3DJpPPXFxEtkGhyFis4xEQiCQW8Gnma7m2N8K/f+mMAgGN8bN2j\n6z3tIRnRPd/qpJhw8G57sYome4aD9RraqzSWz14+B+PPH5y9urKIiK9Pxzm6MVPuxqDC674feqja\njNvp+HQcBzXFmZeeZz36k/EEwx7dJ8tjOLwfhK5nA8dzVyDlkjNSKETsoc3N1Hs1Ho+thzxLMzhM\nNSqj5y7PBQBmsIOifkuuEygOENfZCJrjE8bCQLBnx3M8jFjT7NHhCfb2SQcvUzkqLMp80h0iT4r6\nVRLF4mmMBEwh9KusCHGODILLvQixCMF9HgUOdEp9FavcZrbHoyPsb9+bu43XX1hFJOnZokGG8Q3q\nq5Pb+7gDCs1xxwZ+sZe7Bi6vtU6QQrNHTEQ+Ejnm/hki5HJXTuJCT6h/JrIHN6d1SGQauaH+mZwc\nIq8Q7RuEAZJD8kaFwQhnrtP4vD+8hTM5z5cwxVDN72H8SI0pbbR9qUpPB2WeKWjeNkMvRDZi0cv+\nEF6RgZMZxEX9qkGCtM5p477AjRcopTqMalb5tNVewKUrl+j+jsLBkAQEH+1uIeLUUxhjaxNpaBRV\nQ5NM24yK4Ti2BsO8EJaOUvjs5z4BAPgn/+Q/wx/8AS1G/+sf/jHevUMD0WQBwJMkTYZ28/X8qYyA\n1gbjMU3ael3i8Ijibb76Yz9uY80+KigFjEZMwUhge5cW8wf37+GlVz4JAFhqLyNPabPqjgbIsqJQ\nrYbhjaLX7WP3hNrx8C++jhdeICpQALYDhZTI0xmjlp9BQiAvFOFVBsUWuhhn4J/CUV9i9IjizzxP\nwOe+/NQPf/6xbdxJHGRMvWkIOFmRPRcg5QOAP86w0qNx2kwSXOJ3vul5eLD9kPsqQXWBY0VSoB4W\nAotT1e00TadFqbW0cVXCdWBYXDaejDDmPt873MWt7/9rerZJDxcuEb0bCB+h139s2woEUR0qL5T0\nG8hlIaYrUPWp7a4noZnKrAY1jMbkdtc6Q8wxD5NJhgq7y4XnYfkMxetpYxBzCryMIjjc/2svXMWt\nhzRm8qpEzHUye4MuxqNCmDaFF3CWkePBD5kCgwffq8/dxuXl5VOq5EU/NxoNa7C81zgqrhEz1Ops\nrCG1X//A9VrrU2KbszFZ72cozX5vjDlFQc5LwW9t3sXmuzTGz19fwP4WxdT0D8d49iIb9EKjyTzc\nUWcHjx7SnFterODBCW1iNz5Zgeb+rvRCHB6woel76O/TmOodJ0g5K/D8lVUsr3Is4NhBf0jt6ExG\nWGVJhtWlGkaHZARfW13EMmeINocZhvn8hY4dCFRY6ffsRhvBgKsaTHxETKx4QWD7T6kMHtfkjIIA\naSG2qTM4bIgLraGKA6xwIPkdNhtNe/AzRtvqEvWlps2mjYd9FCvubDxcGIWoVantw/EYgTf/1nq9\nrdBn2ZxxkiDlZ5vEfRiWLEnzCbg4BnJonDClDwG0V8ixMBpP0C+qRAxzCKYmtdYQRZyD48Bl41Hm\nA+iiiLgrIVye9zKEVzgZwqotJmzEaJrZrh2snbs+dxurTQ/Y5jjRwEPX46zZsxWMXKbzlITiKIrq\nmQBRkynUYwPNa9VxPMaf3fwONd0YFIra2SSFKorBQ0BwTUktp6KdWiv4HJvrhi40050//LXn8cra\nFwAAW91jHKa0xhz7PcTqCWq6zn1liRIlSpQoUaJEiR/AR+qZ2mi3rb6HEIDLwpgSBi7TP6E0CDj4\n23gSJ5zNp2SIMbvmu4cnWAkpQG6xsozmEn3+0jPP4eCQqJetR1u4u7UJAKgt1OBx9snGxkU0q3Ry\nWarUEXMZAq0zpEwDnAyHSLlcxlCneIIyS++BsJTlypkF/Ie//u8AAH78a1/Eq69SzZ9Xv/V97O1Q\nIF+WjbCySvTGyy+/gDffotIGv/u7v4u33qJTZ6/XwxmmxL785S//TR/sb4x3X3sNE9YJcl2DR5tE\nLXnVBm6/RQHlUO9AsHs6SwyynDNJpEHKeicP7t3C/dukRTXsj/DNRRZVDSM0NygQeWFlFa6euqpl\nkb2gFIzmGoYS8JivUjO0oNLKZn8arWwdwHk8U9d+8ReQBAXF40KwYKYQgOZsIrx7F/u/97sAAO1m\nOMPj95lE4l8MKcuol4+xxCJ0QlYQusWp15zKMit0iwCDjD1uwvcg+Tjc7xygd0jjtH/Qw+iQguP9\npItKnT5X6lXspvN7ptJcWcpMGwGH2ygzDR0XwqEBenyyj/PMnkq1yeFwoofnurafEzWBExVCoA1E\nRfkc1yDnAFshBZ79zMcAAEoZuHXyWMRphoBp2XiSIeNUVi/QCNmjJ+BD5fN7YrMss5mhUspT1Mss\nJT7rRZr1Cn2Qh2j2+llPVhFErrV+3yDyD6LvZr1gT+KZ6okMV14gaq8WZbhQo1P0QngB2YTojNZi\nhJ1HtCaOJgdgeTAc73Sxcon6+/y5GnxDcy7u5LjdJwHD9uISkozmWT7J4TWpfYdxH5U2vYdVH9jb\no+dtbtTx/IuU9LN96wgLTer7i61F6BFR0+nmAU7G85d2yhIFcCJDGAENppSXqk3U2fvQGw1sXdVJ\nmkIxLVWPqqgU4SMmtolHvi/gMV1V8auoMJ3kBQEU07lRUEGHy0gNhyN4nJgQ1KoQhSZirgCmwaWQ\niDl5KM9z5PjB9/9B+LmvfgrHQ97bxgli9sJs3t1EhzMWx5OJFX/tdg6xy1mKaaaxzDpQsdQYj9jL\nI31bs1TrDIZpxOXVNVy7QokSj+68gSMOMQicBEkR2qIn8AuhN28ZCWfda6Et6yIdwAnmNx/66QAu\nlz4T55cQs8ZkeyNAxBSeqmTI2CQRYxeay43F4wzDDmf2adfWIA29CpXxATCJY+SimEOODeJXBsh5\n7Vc6R8AlwCq1BvKE12Mh0ecg/jhtoJvQs+3cHxVx+3PhIzWmnjl3ARMWQ+yPRshYnNOTEmHRQVqh\nwhl2C+0mNtnYubt9jC4vsP1eH4MVzlBZ0XaDk0GAPVZsfrj/CHs9GnC1Vh0XLhHlt7zQRNWhidFa\nWoPgekFJPMYB1zsaJifwuRernoM4nz/NdRZCTBdvWiTp8/mNMzj/sz8OAPipv/8lxEzhQQhU2dBz\nXImTE5rMGxsb+KM/+iMAVGj1N3/zNwGQwvNHjYPNhzYby3UMPE7FTU7uI360CYBif+woVAamUCE2\nmX3nzdEhnvGI7omrQFYUJO0DeydEk22KClxeBByhLP0nlYHkCRK6QCSLzU1D62JBU8iKgpVGQxZ1\nAP/Rf/zYNobra/j+Lar9uLa8jGc/QVRanuVIWJIhzHOcVDjraWTAeoa4oH2cqxZxVR5yHl9QuY0X\nE8AMLaFmNlkDVdB8KseI60O+9d3v4J3XiZLZ3d/DJS4OfHHtGgYPib7uuURnz4veqAfDsQG1aMUW\nEHaVRsNn5f3eCEWZqtCvwnCdMz8IMOG4LRNrpJzCnAcxYp/j40wGT7FqsagiZcrErYR4/uMvcZ8A\nESstD5KepSXq9QaGIxr7nutDaM7M8SNkev6TTZIk0yxIrW0m3Xsz5t5PLmTW8Jo1mt573Sw+SG7k\ndCzj+//d7P+bVZn+MChnqqq/9e49PPtJ2iTPnm/ju69/FwCgZQ2HJ/QSkzzG+iodKh+eJBgc0u/c\nzk7gM5UzmiToJfROmtLByjk63J2YEdwFNmRrHioe3dPLu3jhJbrmwvkN9Ca0mafnQzy3fBEAsFGt\nwMsoJuXe9j7UE4g9jpKRNV6Uzmw6vuM68HitjDzAhDR2okzbwthxEsMUArq+C48NJdc38DnLE0bA\nLaRYBKB4fRpnE6u2Pp6MoNhIqVQqkLxXpVluJRaMkFCFfIkxVsZlHjQbFYQcq7ViBBKedBdWW4j5\neaTroXtMc+Jg9wjv3KY18v6DTUh2PiTjBJJVzJVJbQ1Mz5M2Q951Ejg8X8+sr8IoWi9bTg9j3kd1\nJUbQoPflu8Yq5SuTQ+hC+NZAiCcorL41QI2dJ724j84W9dti00HARnd/3IcGG8JyxTpYTGbQPyp0\nggQMzw8llDVZPc+3osXCkKgrQMLJDRYMTuIEGRuqk+EImte8oF7HgKUgjk4OMTmiNebo4RAnx8nc\nbSxpvhIlSpQoUaJEiafAR+qZcqREnevnuJ5vdZ1qYYSAvQtq0IPLruUgCtFmEb07u52ZSt8hNLsw\n4zhBGJFVPxyNcNIj2iNJU0w4SNZAY3JCno+JFsjYAvddiZCzvHzh4YT1T+QkQVSkOrkBxJzZNR8G\nOl1xRoWWVrzU83L4rSK7RdpTaa4yLHB9uN/6rd/Cr/7qrwKgk1GhnfMklMD/V+jcexUmL8r9xFCs\ng5InYzjsWoXJkMWFAFwMY/hUkadImOZTRlvaaJy6tiaT74SosGha1asjy+izUCMoPkk4jme1YTQU\nkiLIWAAoqqAbwCsyGo2VlZkL3f4Jtjl4Xccxrjz/HACg5oSYcIBnlsUAB+1W11ax06HvHwJwOWGh\nqms4GZF3dDg4xvoSeRKFEKhxqRilNQI+MTuuA5efX7rA/n3y3O3ev4vOIZ1E948OEVaoMSu1OhYb\nND8ebD/EMJo/003nGhWPa4/VljBKaN7kyQiLXJPssJOiyvXMKo0GekPy3MbxEF2eZ6uLq8iZ5nN9\nqu0HACI3WG0R5SONQcCBn/V6E4bncRqn6HXoPirViLgfpHRQb5B3LAgdQPCa4QSQzvxe4myGn5+l\n+T6I2pv9ft559X7erPcGqL/fPd9LBc5msM7rmaqmFRxskWeyqT0kE04icHZw4RkSCz7Y7UNzVO/o\nyIVkAcyw7kCwYOpb20dIOaB5cWURjRUaU6+cvYTlDRqzH8PfwdbgNgDgcHAXoz5TzTiDy2fpt5xY\nYMzeh4W1GoKIhZKDHMf7XCMxF1i9cXGu9gGA9AVk4THJgApTwVmaosf7hPFduA6vHwoI2WOlVG77\nMk0SDEbTUkFOUHiXNAoCQWmNJC/WJwOPPcwrCw1I1tNLc4WBLTkU2NJR40kMx2fBUqUw4ZCUeWDc\nCoRhvSeTQ7J3Oqo6UHVurwKqLu0H64stPPcMaYTtHe5BFRnMxyPcuU/6Ypu7B9jbY5qyP0F/QPuf\nMCMMORkrGysEzBqcc3rwatRXC1cjgEvSHRuFCVNy2niQpvg8rZk4D9ReBvBefrQuAA4VOckU9IQ8\n2KNuAm+Fwwe8ARTTcDo16OzT+Kwv1WyoSL97bNkKz/Osl0prjYhtghwKcbEmCQeTMb27eDC0mnLt\n9UX4rB1mBilcZl4WQm33unnwkRpT29vbcH2a2F4QoMEqrrVqDSgUYx0HUnFqq+thgetUnT+v4HId\nOiMkPL5PHCeIePM6Oe4hYd56ebmNTNE9W/U6FiMKFggVkHZoYGVJDIc3ryCQkJxiXxceMjZ20jhG\nHs8f0f/BmInXkICwXe9YNUoDwHGk/X52sS0MK+B0xtFHja23/x/IwoUtchSO1lwZiGJTFRKF4Shn\nVBqEkIAo6vq5YHsY0glgxJSCifj6SmRgWLVYJSF0Nm33KSHEQjJDGBQxDGb6sxBCwn2CvlryXXz6\nKomO1qMAFd6AAgO0XKZMRIqMqYXl9jJuphT3thMrKIfHlPStWrLSIyt4B5AgKQAYKawYaZqmlsaS\nSiAMaUFYbJ9BjaUCmvt7uL+5CQB47W6MH/r05wAAK+0VrNcbc7ex4jbQ8GhMDft9gLNrpBdj+5Bi\nZiK/hhqnnE8mA5t1KhwgKKhMN0Dg0jUaCg7LSDheE1VWGx7FexBsAKoxIPg9JpOEgi8AyMyBx4t2\nnI6gOIvJZBKZJoPac1vIkvnf42ym5Hsz9h4nZvtemm42++/9MEsFvteYmo2Hmr3m/aQRlFKnsgI/\nDLu3HyHj3LLlsyG2OzQGR50JltZoNxx0NQa8cQnjQnIxXkiNIvW1vpihusTFb50WDMfSHcQxlvi9\nnV+rY/M12pzX3AYa623unBoWuXDu5PAACOlvTw73sH/MYRy1Fr67STR10nRQiS7P1T4AvHHOULts\nOORa2ZgXJWCrL3iQqHDcntbSGjXCkXbvyfLczr84zSFZiNd1HXiF9EkSI+Y9SSmFOm/Ooe9jYgUw\nDRRTcpM0hubrjTaI0/npIQ1hZV+EEfDYQFNKWaeBowVct4gNVZDsBDi/sWIPDSsLC3j+BhlZE6Vx\ncEQGyO7ukVU9335wz9YUPdApQlD/tM0YPljweBBjxFlswdIVGK70AG2mh1hj8AQsHwLHRb/ITAyb\niDTtwekww8ERH55dBb1LY+zIm4rdBvUIMYcHhUmAgCuSZHGGQjpeBoBX7C05kKKIzZZIuXYhtOK9\niSpqLJ2hNXXVW0S1S2NgJWpC1TiLc9VFszV/I0uar0SJEiVKlChR4ikg5i1dUKJEiRIlSpQoUeIH\nUXqmSpQoUaJEiRIlngKlMVWiRIkSJUqUKPEUKI2pEiVKlChRokSJp0BpTJUoUaJEiRIlSjwFSmOq\nRIkSJUqUKFHiKVAaUyVKlChRokSJEk+B0pgqUaJEiRIlSpR4CpTGVIkSJUqUKFGixFOgNKZKlChR\nokSJEiWeAqUxVaJEiRIlSpQo8RQojakSJUqUKFGiRImnQGlMlShRokSJEiVKPAVKY6pEiRIlSpQo\nUeIpUBpTJUqUKFGiRIkST4HSmCpRokSJEiVKlHgKuB/lj33jte8az/MBAJ7nw3EcegjXs58dx7Gf\npZSQUk4/Q9h72e/Ne3/lB76gb42Z/Yf9TmvNX2oYQ5+1VvZ7AwWtFQDgyvkr0wf4AHzlhz5nPvPD\nXwQA/MzP/yJe/6vvAQAatQYuXr4KAMiURqPZBAAEQQiji2czEMUvCAOtp89cPL9W5nRbGEprqOL7\nmf8vIGFA/Wm0hub/JwAI6OJnYbjfPvGJqx/axv/0H/8npng/nudhOBwCAPJcww+q1NZGA55P10wm\nI9uvSZYhyTIAwIXzZxGGAQBgPBpif28HACBFjOefv8b3F/B8w+1LIQS/kzxDxvfJM400pc9hEEHz\nNblWcFx6Bq01lKJ3+G/90u889h1uHx8Yzff/33//f8N/90//GwDA0dEh5PQFQRXvx3CHAhAQti9h\nBMB9/1FiZ2fnsW1sryybJEkAAHmeQ+XUb74XIFc5ACDLMjvWjDEQYnrb6dx17fez/19rbf9tjEGa\npvR30oUj6XulMyhNvxX6PsBzeuH8KtbOr9GzJTGOd48AAL3OCNmE3st40H9sG2+stUzoePycAi4f\nHYUGIo//4Qi7rgSOC5+/v3zjBXzmyz8BANh/tAPfpXXL931ofr+O48J1aQlNsxQnfZoLFy5cRsZL\n6yjP8SNf/FEAwNXLl9E5PgEA3Lr5fbzzBq0NaTxCzuMtThNsbu8DAP6r//a//9A2bqydMZVqCADw\nPAce91+jWsPGOvVfmiQ4OD6yz1vxI+q/LMMJz11fevB96qc4juFw1yw1Kjjsden6VMMH9YHRBteu\nXgAAqDzDm7fvAQAmWsMPeN4PR8gTeufXr16BcKgpb96+jeFoAgDIJslj3+F/9I/+oRkNaZyOxzkC\nXjOaywkg6P7GTMeaKyUkL2upVnZNFAIQPMYXHR81GnaoS4nFRh0AsLZxBpWQ+vPk6BiPtmhNOjk+\nQaNB67WRBg77IFzjwDj8/qMAW2oMAOjkMY571Lf/7L/+k8e28d/7lZ81QtH7jwIXgu8pXAlj6EG1\nySB4nKpMQClqV5ooOJLendIZgoDekVJquk84EsXGIqWw66UQsNdrAyRxwtdIOPxbruPYawABx6Hn\nEY6C41Ff/ef/xe89to0ATLEGz64Tf2sw5gMshfeH4ziPfeiP1Jgi42i68BYGkRDi1IJsO/u9nT6z\nYdktbeZ6GjzF/zltiLzfCzy9+E//9vQ98UH22fsiqFQxGdDi9e2v/z52tmlCNlpLeLj9NgCg0x/j\ncz/0dwEAz994GVmWv//zyMK401Bs0AnMdst045Yz/WmMgWJjUAgBURiJUkPMGFMSP2isPQ5SKuQ5\nLWLjcc/+Zq1atT0vTY5KQItevdKCw9coLRCEZHC5noTWNKmPhwOEPl1fiRagswr97UITxsQAgCQb\nIgppuKpsjCzt0X1caY014SjAUD9JxyBNk5nnnt8Ja7QADF3f7w3R748AAFoDRlA/ZTMLuAPQagRA\nGQDudPzKWYPrffC3tbD0+31EEW2s9XoNwwFtBJN4cuq6D3o+a9xrferwk+fTsTxriBUdIISA4N3a\nkS5MTu+r2qghqtcAAEma4f67dwEAnuugyhvo6uoyDnYO525jvdZEnQ8t3d4RckXjxBUGQ37OzkDb\nd3N5oYJxTmNymAPXrz0DADjpdDAZ0eYYVip2DrmuA5cN9v39DpY3ztJ9Ll3Et2/SXO9o5sE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S0ofmPTOIcJCKUofFxESQ7NjpsFl/ZhEEvS4YAkyzIofleUcJFOV2b7Eezn2cAQ8hcdAi20gUEH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\nz59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ST3Cyj7rX57aEmI2oD622TqwVwjovNyWFK4g3psx8SSmdJh5gXIb5ebG2toI6W11t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Xn1ghK4NiU8dux9/dXrjv6ZJzn+9vv/QJ/nc2Ss9czSwtW0C4K6mj2Eh05lCi0lStYQizLH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PiwwFplniqDUEKkNKfY+naH/hnvna175Gz0Dif/nffnPpOjYwXyONNNJII4000shnkH8p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\nC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YSBLwkhyJPCBlO7joBrCLmtqyZQG9PqeogZVCKV9UaIZ54hlOWxy+fhlpkKQkMYutfx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\nPDw8PDw8LgAfTHl4eHh4eHh4XAD/G1FyuH7PWodbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f77243df610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 130.64189796  135.98173469  132.47391837  130.05569388  135.34804082\n",
      "  131.75402041  130.96055102  136.14328571  132.47636735  131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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58v8P8KWI+Dyj5bivkHQp8F3gnoj4XeA94KbZwzGzoYxN/hj57+bLk5t/AXwJ+GGzfSdw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75e8626450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.008204\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -9.133311 analytic: -9.133311, relative error: 1.061246e-12\n",
      "numerical: 8.772405 analytic: 8.772405, relative error: 1.766798e-11\n",
      "numerical: -4.010682 analytic: -4.010682, relative error: 8.246785e-11\n",
      "numerical: -15.655272 analytic: -15.655272, relative error: 6.077577e-12\n",
      "numerical: -5.652935 analytic: -5.652935, relative error: 1.760941e-11\n",
      "numerical: 28.551199 analytic: 28.551199, relative error: 5.392556e-12\n",
      "numerical: -10.332257 analytic: -10.332257, relative error: 2.830934e-11\n",
      "numerical: -62.437389 analytic: -62.437389, relative error: 4.479770e-12\n",
      "numerical: -15.785602 analytic: -15.742619, relative error: 1.363325e-03\n",
      "numerical: -2.077387 analytic: -2.077387, relative error: 5.617955e-13\n",
      "numerical: -25.965641 analytic: -25.937231, relative error: 5.473650e-04\n",
      "numerical: -12.063185 analytic: -12.063185, relative error: 9.072343e-13\n",
      "numerical: -11.464814 analytic: -11.464814, relative error: 6.724236e-12\n",
      "numerical: 0.689563 analytic: 0.689563, relative error: 6.214115e-10\n",
      "numerical: 11.443419 analytic: 11.443419, relative error: 1.421608e-11\n",
      "numerical: -4.044108 analytic: -4.044108, relative error: 8.392820e-11\n",
      "numerical: -2.144499 analytic: -2.144499, relative error: 1.117075e-10\n",
      "numerical: 19.395010 analytic: 19.395010, relative error: 1.778005e-11\n",
      "numerical: 9.373141 analytic: 9.383472, relative error: 5.507945e-04\n",
      "numerical: -7.919425 analytic: -7.919425, relative error: 4.556417e-11\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** *fill this in.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.008204e+00 computed in 0.242610s\n",
      "Vectorized loss: 9.008204e+00 computed in 0.021308s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.196322s\n",
      "Vectorized loss and gradient: computed in 0.012032s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 411.440546\n",
      "iteration 100 / 1500: loss 242.613419\n",
      "iteration 200 / 1500: loss 148.568760\n",
      "iteration 300 / 1500: loss 91.279722\n",
      "iteration 400 / 1500: loss 56.794937\n",
      "iteration 500 / 1500: loss 35.555272\n",
      "iteration 600 / 1500: loss 23.766768\n",
      "iteration 700 / 1500: loss 16.494195\n",
      "iteration 800 / 1500: loss 11.854640\n",
      "iteration 900 / 1500: loss 8.676771\n",
      "iteration 1000 / 1500: loss 7.598624\n",
      "iteration 1100 / 1500: loss 6.392914\n",
      "iteration 1200 / 1500: loss 6.252726\n",
      "iteration 1300 / 1500: loss 5.594411\n",
      "iteration 1400 / 1500: loss 5.200172\n",
      "That took 6.497046s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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tjQa/owAAgBhCOfPR5QtLtLamVc2dfX5HAQAAMYJy5qND151tqeO6MwAAMIhy5qMFZTlK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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75ea5d9ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.384980\n",
      "validation accuracy: 0.389000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "learning_rates ,regularization_strengths:0.000000,25000.000000 \n",
      "iteration 0 / 1500: loss 409.374302\n",
      "iteration 100 / 1500: loss 243.983821\n",
      "iteration 200 / 1500: loss 148.422350\n",
      "iteration 300 / 1500: loss 91.759237\n",
      "iteration 400 / 1500: loss 56.622095\n",
      "iteration 500 / 1500: loss 36.274702\n",
      "iteration 600 / 1500: loss 24.192938\n",
      "iteration 700 / 1500: loss 16.323556\n",
      "iteration 800 / 1500: loss 11.997474\n",
      "iteration 900 / 1500: loss 8.834935\n",
      "iteration 1000 / 1500: loss 7.836551\n",
      "iteration 1100 / 1500: loss 6.777392\n",
      "iteration 1200 / 1500: loss 5.993815\n",
      "iteration 1300 / 1500: loss 5.210511\n",
      "iteration 1400 / 1500: loss 5.773562\n",
      "\n",
      "learning_rates ,regularization_strengths:0.000050,50000.000000 \n",
      "iteration 0 / 1500: loss 799.018296\n",
      "iteration 100 / 1500: loss 424208376849286758536918692349584867328.000000\n",
      "iteration 200 / 1500: loss 70118247241734490541618382762105198265507645809869846042635800239103016960.000000\n",
      "iteration 300 / 1500: loss 11589984697543509305657032935338204583988643579817242464180410055725296954891048032855136592998225522540412928.000000\n",
      "iteration 400 / 1500: loss 1915731647230062625242955680066669472223400094247671867475764424767732369901258786472059656484173991004736639789593057115479700143078174701387776.000000\n",
      "iteration 500 / 1500: loss 316655098343371193171397375139940539890107770441669176080579011703594527478978050888963842716176646345858906945636586301078189327658940281773181840591417936207233780714868412776448.000000\n",
      "iteration 600 / 1500: loss 52340551690436480537134242256427495992733312758014019773516184370616954305270550836729914072175544768269168153318410451056890299456377509759266029384232628741886311230337941411392041695912501810986773482681346818048.000000\n",
      "iteration 700 / 1500: loss 8651474003076325453445341963790714755835706598319070229438165533180503640931007235175483374204257448861819042207348604680644542800697586277384389073229708488466928291419035320974692446099498719693485191500125192940021607163326806854231215553663467520.000000\n",
      "iteration 800 / 1500: loss 1430019363735164508142525677051700667748565392219792061968044031436067220358602641685424227642826914785382891585439458230761559532120218566396769396681029252489251567706629094773071136935333404511883543561959284733847187861038738336510134120939624722411678015832400233791409642482434048.000000\n",
      "iteration 900 / 1500: loss inf\n",
      "iteration 1000 / 1500: loss inf\n",
      "iteration 1100 / 1500: loss inf\n",
      "iteration 1200 / 1500: loss inf\n",
      "iteration 1300 / 1500: loss inf\n",
      "iteration 1400 / 1500: loss inf\n",
      "\n",
      "learning_rates ,regularization_strengths:0.000000,20000.000000 \n",
      "iteration 0 / 1500: loss 328.630902\n",
      "iteration 100 / 1500: loss 216.880659\n",
      "iteration 200 / 1500: loss 145.414001\n",
      "iteration 300 / 1500: loss 98.654042\n",
      "iteration 400 / 1500: loss 67.456778\n",
      "iteration 500 / 1500: loss 45.967166\n",
      "iteration 600 / 1500: loss 33.230046\n",
      "iteration 700 / 1500: loss 23.561326\n",
      "iteration 800 / 1500: loss 16.564409\n",
      "iteration 900 / 1500: loss 12.910510\n",
      "iteration 1000 / 1500: loss 10.749321\n",
      "iteration 1100 / 1500: loss 8.620204\n",
      "iteration 1200 / 1500: loss 7.164344\n",
      "iteration 1300 / 1500: loss 6.544472\n",
      "iteration 1400 / 1500: loss 5.713450\n",
      "\n",
      "learning_rates ,regularization_strengths:0.000001,20000.000000 \n",
      "iteration 0 / 1500: loss 327.031231\n",
      "iteration 100 / 1500: loss 11.055700\n",
      "iteration 200 / 1500: loss 5.386385\n",
      "iteration 300 / 1500: loss 5.683318\n",
      "iteration 400 / 1500: loss 6.266613\n",
      "iteration 500 / 1500: loss 5.942279\n",
      "iteration 600 / 1500: loss 4.968671\n",
      "iteration 700 / 1500: loss 5.856363\n",
      "iteration 800 / 1500: loss 6.239960\n",
      "iteration 900 / 1500: loss 6.468359\n",
      "iteration 1000 / 1500: loss 5.991373\n",
      "iteration 1100 / 1500: loss 5.919386\n",
      "iteration 1200 / 1500: loss 5.981070\n",
      "iteration 1300 / 1500: loss 6.607455\n",
      "iteration 1400 / 1500: loss 6.575978\n",
      "lr 1.000000e-07 reg 2.000000e+04 train accuracy: 0.377612 val accuracy: 0.376000\n",
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.382898 val accuracy: 0.397000\n",
      "lr 1.000000e-06 reg 2.000000e+04 train accuracy: 0.265163 val accuracy: 0.275000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.062714 val accuracy: 0.053000\n",
      "best validation accuracy achieved during cross-validation: 0.397000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-5,1e-7,1e-6]\n",
    "regularization_strengths = [2.5e4, 5e4,2e4,2e4]\n",
    "# learning_rates = [1e-7, 5e-5]\n",
    "# regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "\n",
    "for lr,reg in zip(learning_rates,regularization_strengths):\n",
    "    \n",
    "    print ('\\nlearning_rates ,regularization_strengths:%f,%f '%(lr,reg) )\n",
    "    \n",
    "    test_svm = LinearSVM()\n",
    "    test_svm.train(X_train,y_train,lr,reg,num_iters=1500, verbose=True)\n",
    "    \n",
    "    y_train_pred = test_svm.predict(X_train)\n",
    "    train_accuracy = np.mean(y_train_pred == y_train)\n",
    "    \n",
    "    y_val_pred = test_svm.predict(X_val)\n",
    "    val_accuracy = np.mean(y_val_pred == y_val)\n",
    "    \n",
    "    results[lr,reg] = (train_accuracy,val_accuracy)\n",
    "    if(val_accuracy > best_val):\n",
    "        best_val = val_accuracy\n",
    "        best_svm = test_svm\n",
    "    \n",
    "    \n",
    "\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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2l0naGbhK0s22b++vcd2ExvYGSUdRDAyOiIiIUaBiQrPSdr0Z/pdRzF3XY2q5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\nOl4SmoiIiOh4SWgiIiKi4/1/2ltKHQCpc7oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75e83e9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.368000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h/GVpHUmuOMj1h69RrO/yDcbsM1NWu1V51Wqn60iKbj+yyqBy9psrinzrUDJm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ji1zmff6MPF/mIvbu6jJPUE/RZZoN8eHvfO17uNhBu/gLxxdwVXf1c7ckh6DdvfHUeSuD3BuN5N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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75e885d4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
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   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** *fill this in*"
   ]
  }
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